chngs to docs

Author: gaddams

docs/source/classes/tangram.plot_utils.plot_cell_annotation_sc.rst

@@ -0,0 +1,6 @@
+tangram.plot\_utils.plot\_cell\_annotation\_sc
+==============================================
+
+.. currentmodule:: tangram.plot_utils
+
+.. autofunction:: plot_cell_annotation_sc

docs/source/classes/tangram.plot_utils.plot_genes_sc.rst

@@ -0,0 +1,6 @@
+tangram.plot\_utils.plot\_genes\_sc
+===================================
+
+.. currentmodule:: tangram.plot_utils
+
+.. autofunction:: plot_genes_sc

docs/source/classes/tangram.plot_utils.rst

@@ -27,10 +27,14 @@
 
     plot_cell_annotation
 
+    plot_cell_annotation_sc
+    
     plot_gene_sparsity
 
     plot_genes
 
+    plot_genes_sc
+    
     plot_test_scores
 
     plot_training_scores

docs/source/getting_started.rst

@@ -17,8 +17,13 @@ Cell Level
 **************************
 To install Tangram, make sure you have `PyTorch <https://pytorch.org/>`_ and `scanpy <https://scanpy.readthedocs.io/en/stable/>`_ installed. If you need more details on the dependences, look at the `environment.yml <https://github.com/broadinstitute/Tangram/blob/master/environment.yml>`_ file. 
 
+Create a conda environment for Tangram::
+
+    conda env create --file environment.yml
+
 Install tangram-sc from shell::
-    
+
+    conda activate tangram-env
     pip install tangram-sc
     
 Import tangram::

docs/source/news.rst

@@ -5,6 +5,6 @@ Tangram News
 
 - On Mar 9th 2021, Nicholas Eagles wrote a `blog post <http://research.libd.org/rstatsclub/2021/03/09/lessons-learned-applying-tangram-on-visium-data/#.YPsZphNKhb->`_ about applying Tangram on Visium data.
 
-- The Tangram method has been used by our colleagues at Harvard and Broad Institute, to map cell types for the developmental mouse brain -see Fig. 2 [`Nature(2021)<https://www.nature.com/articles/s41586-021-03670-5>`_]
+- The Tangram method has been used by our colleagues at Harvard and Broad Institute, to map cell types for the developmental mouse brain -see Fig. 2  (`Nature(2021) <https://www.nature.com/articles/s41586-021-03670-5>`_ )
 
 - Tangram is now officially a part of `Squidpy <https://squidpy.readthedocs.io/en/stable/index.html>`_

tangram/utils.py

@@ -73,13 +73,13 @@ def get_matched_genes(prior_genes_names, sn_genes_names, excluded_genes=None):
         prior_genes_names (sequence): List of gene names in the spatial data.
         sn_genes_names (sequence): List of gene names in the single nuclei data.
         excluded_genes (sequence): Optional. List of genes to be excluded. These genes are excluded even if present in both datasets.
-            If None, no genes are excluded. Default is None.
+        If None, no genes are excluded. Default is None.
 
     Returns:
         A tuple (mask_prior_indices, mask_sn_indices, selected_genes), with:
-            mask_prior_indices (list): List of indices for the selected genes in 'prior_genes_names'.
-            mask_sn_indices (list): List of indices for the selected genes in 'sn_genes_names'.
-            selected_genes (list): List of names of the selected genes.
+        mask_prior_indices (list): List of indices for the selected genes in 'prior_genes_names'.
+        mask_sn_indices (list): List of indices for the selected genes in 'sn_genes_names'.
+        selected_genes (list): List of names of the selected genes.
         For each i, selected_genes[i] = prior_genes_names[mask_prior_indices[i]] = sn_genes_names[mask_sn_indices[i].
     """
     prior_genes_names = np.array(prior_genes_names)
@@ -115,8 +115,8 @@ def one_hot_encoding(l, keep_aggregate=False):
 
     Returns:
         A DataFrame with a column for each unique value in the sequence and a one-hot-encoding, and an additional
-            column with the input list if 'keep_aggregate' is True.
-            The number of rows are equal to len(l).
+        column with the input list if 'keep_aggregate' is True.
+        The number of rows are equal to len(l).
     """
     df_enriched = pd.DataFrame({"cl": l})
     for i in l.unique():
@@ -137,7 +137,7 @@ def project_cell_annotations(
         adata_sp (AnnData): spatial data used to save the mapping result.
         annotation (str): Optional. Cell annotations matrix with shape (number_cells, number_annotations). Default is 'cell_type'.
         threshold (float): Optional. Valid for using with adata_map.obs['F_out'] from 'constrained' mode mapping. 
-                           Cell's probability below this threshold will be dropped. Default is 0.5.
+        Cell's probability below this threshold will be dropped. Default is 0.5.
     Returns:
         None.
         Update spatial Anndata by creating `obsm` `tangram_ct_pred` field with a dataframe with spatial prediction for each annotation (number_spots, number_annotations) 
@@ -797,10 +797,10 @@ def df_to_cell_types(df, cell_types):
 
     Args:
         df (DataFrame): Columns correspond to cell types.  Each row in the DataFrame corresponds to a voxel and
-            specifies the known number of cells in that voxel for each cell type (int).
-            The additional column 'centroids' specifies the coordinates of the cells in the voxel (sequence of (x,y) pairs).
+        specifies the known number of cells in that voxel for each cell type (int).
+        The additional column 'centroids' specifies the coordinates of the cells in the voxel (sequence of (x,y) pairs).
         cell_types (sequence): Sequence of cell type names to be considered for deconvolution.
-            Columns in 'df' not included in 'cell_types' are ignored for assignment.
+        Columns in 'df' not included in 'cell_types' are ignored for assignment.
 
     Returns:
         A dictionary <cell type name> -> <list of (x,y) coordinates for the cell type>

tangram_tutorial.ipynb

@@ -1742,7 +1742,7 @@
    "hash": "3ac21f8bf730324e87b2ab04b60f02f57ab9f380889952ce3b825e9207797ed6"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1756,7 +1756,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

tutorial_sq_tangram.ipynb

@@ -515,14 +515,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***\n",
-    "## The _Tangram_ trick: scRNA-seq are the new spatial data\n",
+    "****\n",
+    "\n",
+    "### The _Tangram_ trick: scRNA-seq are the new spatial data\n",
     "\n",
     "_Tangram_ learns a spatial alignment of the single cell data by looking at a subset of genes, specified by the user, called the training genes. Training genes need to bear interesting signal and to be measured with high quality. Typically, we choose the training genes are 100-1000 differentially expressedx genes, stratified across cell types. Sometimes, we also use the entire transcriptome, or perform different mappings using different set of training genes to see how much the result change.\n",
     "\n",
     "_Tangram_ fits the scRNA-seq profiles on space using a custom loss function based on cosine similarity. The method is summarized in the sketch below:\n",
     "\n",
-    "![title](figures/how_tangram_works.png)"
+    "![title](figures/how_tangram_works.png)\n"
    ]
   },
   {
@@ -1445,8 +1446,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***\n",
-    "## Deconvolution\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deconvolution\n",
     "\n",
     "For untargeted spatial technologies, like Visium and Slide-seq, a spatial voxel may contain more than one cells. In these cases, it might be useful to disentangle gene expression into single cells - a process called deconvolution. Deconvolution is a requested feature, and also hard to obtain accurately with computational methods. If your goal is to study co-localization of cell types, we recommend you work with the spatial cell type maps instead. If your aim is discovery of cell-cell communication mechanisms, we suggest you compute gene programs, then use `project_cell_annotations` to spatially visualize program usage. To proceed with deconvolution anyways, see below.\n",
     "\n",